A Surrogate Visualization Model Using the Tensor Train Format
| Autor: | E. G. Paredes, Rafael Ballester-Ripoll, Renato Pajarola |
|---|---|
| Přispěvatelé: | University of Zurich |
| Rok vydání: | 2016 |
| Předmět: |
Theoretical computer science
1707 Computer Vision and Pattern Recognition 10009 Department of Informatics Computation Score 020207 software engineering 02 engineering and technology Parameter space 000 Computer science knowledge & systems Grid Linear subspace 1704 Computer Graphics and Computer-Aided Design 1712 Software 1709 Human-Computer Interaction Tensor (intrinsic definition) 0202 electrical engineering electronic engineering information engineering A priori and a posteriori 020201 artificial intelligence & image processing Algorithm Parallel coordinates Mathematics |
| Zdroj: | SIGGRAPH Asia Symposium on Visualization |
| DOI: | 10.5167/uzh-129611 |
| Popis: | Complex simulations and numerical experiments typically rely on a number of parameters and have an associated score function, e.g. with the goal of maximizing accuracy or minimizing computation time. However, the influence of each individual parameter is often poorly understood a priori and the joint parameter space can be difficult to explore, visualize and optimize. We model this space as an N-dimensional black-box tensor and apply a cross approximation strategy to sample it. Upon learning and compactly expressing this space as a surrogate visualization model, informative subspaces are interactively reconstructed and navigated in the form of charts, images, surface plots, etc. By exploiting efficient operations in the tensor train format, we are able to produce diagrams such as parallel coordinates, bivariate projections and dimensional stacking out of highly-compressed parameter spaces. We demonstrate the proposed framework with several scientific simulations that contain up to 6 parameters and billions of tensor grid points. |
| Databáze: | OpenAIRE |
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